Background: Artificial intelligence is increasingly being used for analyzing image data in medicine. Objectives: We aimed to develop a computer vision artificial intelligence (AI) application using limited training material to aid in the multi-label, multi-disease diagnosis of chest X-rays. Methods: We trained an EfficientNetB0 pre-trained model, leveraging transfer learning and deep learning techniques. Six thoracic disease categories were defined, and the model was initially trained on images sourced online and chest X-rays from a hospital database for training and internal validation. Subsequently, the model underwent external validation. Results: In constructing and validating Deep Chest, we utilized 453 images, achieving an area under curve (AUC) of 0.98, sensitivity of 0.98, specificity of 0.80, and accuracy of 0.83. Notably, for diagnosing masses or nodules, the sensitivity, specificity, and accuracy were 0.97, 0.81, and 0.83, respectively. We deployed Deep Chest as a free experimental web application. Conclusions: This tool demonstrated high accuracy in diagnosing both single and coexisting pulmonary pathologies, including pulmonary masses or nodules. Deep Chest thus represents a promising AI-based solution for enhancing diagnostic capabilities in thoracic radiology, with the potential to be utilized across various medical disciplines, especially in scenarios where expert support is limited.

The authors have declared no competing interest.

This study did not receive any funding

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Vast majority of our dataset includes open source chest x-rays, however, additional limited number of patient chest x-rays were used to enrich the dataset. Again, limited retrospective external database of chest x-rays were used for validation purposes. For the limited number of real patients whose chest x-rays were used, due to the retrospective nature, it was not possible to obtain consent from these cases as some of them had unfortunately died at the time of data collection. All chest X-rays used for this study had been de-identified. Thus, no ethical committee approval or consent was obtained.

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All data produced in the present study are available upon reasonable request to the authors